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Mondani H, Holme P, Liljeros F (2014) Fat-Tailed Fluctuations in the Size of Organizations: The Role of Social Influence. P...
Mondani H, Holme P, Liljeros F (2014) Fat-Tailed Fluctuations in the Size of Organizations: The Role of Social Influence. P...
Local trade unions in Sweden, 1880–1939 -Long quiet periods -Large jumps F Liljeros, The complexity of social organizing, ...
Local trade unions in Sweden, 1880–1939 F Liljeros, The complexity of social organizing, Ph.D. thesis 2001. Examples
Local trade unions in Sweden, 1880–1939 F Liljeros, The complexity of social organizing, Ph.D. thesis 2001. Examples
Growth rate US firms Buldyrev & al., J Phys I France 7 (1997), 635–650. Examples
Growth rate Italian firms Bottazzi, Secchi, Physica A 324 (2003), 213–219. Examples
Examples Growth rate Italian firms Bottazzi, Secchi, Physica A 324 (2003), 213–219.
MHRStanley&al,1996Nature379:804–806. Growthrate(someothersetof)USfirms Examples
Universality
Previous models
Previous models Economic models
Previous models Economic models Doesn’t fit e.g. voluntary organizations
Physics models Previous models
Physics models Not without problems either… Previous models
Stochastic models Previous models
Stochastic models Previous models
Stochastic models Original has log- normal growth rate distribution Previous models
The SAF model Assumptions -N individuals connected in a network -G organizations -Each time step an agent changes organiza...
The SAF model Assumptions -N individuals connected in a network -G organizations -Each time step an agent changes organiza...
The SAF model Assumptions -N individuals connected in a network -G organizations -Each time step an agent changes organiza...
The SAF model Assumptions -N individuals connected in a network -G organizations -Each time step an agent changes organiza...
The SAF model Schwartzkopf, Axtell, Farmer, arxiv:1004.5397. cf. threshold models (Prof. Kertesz’s talk)
The SAF model Schwartzkopf, Axtell, Farmer, arxiv:1004.5397. cf. threshold models (Prof. Kertesz’s talk)
Our extended SAF model Additional assumptions -Trying different networks -Organization cannot die (if the last person leave...
Results Tent plot, ER model δ = 1.
Results Tent plot, directed ER model δ = 1.
Results Tent plot, scale-free networks, δ = 1.
Results Tent plot, directed scale-free networks, δ = 1.
Results 2D grid, δ = 0
Results 2D grid, δ = 1
Results 2D grid, δ = 10
Conclusions -The SAF model works and it is independent of the network topology (it just needs a (strongly connected giant ...
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Modeling the fat tails of size fluctuations in organizations

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Invited at Physics of Social Complexity (PoSCo), Pohang, Korea, January 28 2015. Presenting the paper by Mondani, Holme, Liljeros (2014) http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0100527

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Modeling the fat tails of size fluctuations in organizations

  1. 1. Mondani H, Holme P, Liljeros F (2014) Fat-Tailed Fluctuations in the Size of Organizations: The Role of Social Influence. PLoS ONE 9(7): e100527. Modeling the fat tails of size fluctuations in organizations Petter Holme
  2. 2. Mondani H, Holme P, Liljeros F (2014) Fat-Tailed Fluctuations in the Size of Organizations: The Role of Social Influence. PLoS ONE 9(7): e100527. Modeling the fat tails of size fluctuations in organizations Petter Holme
  3. 3. Local trade unions in Sweden, 1880–1939 -Long quiet periods -Large jumps F Liljeros, The complexity of social organizing, Ph.D. thesis 2001. Typical data: time series of sizes (not join / quit numbers) Examples
  4. 4. Local trade unions in Sweden, 1880–1939 F Liljeros, The complexity of social organizing, Ph.D. thesis 2001. Examples
  5. 5. Local trade unions in Sweden, 1880–1939 F Liljeros, The complexity of social organizing, Ph.D. thesis 2001. Examples
  6. 6. Growth rate US firms Buldyrev & al., J Phys I France 7 (1997), 635–650. Examples
  7. 7. Growth rate Italian firms Bottazzi, Secchi, Physica A 324 (2003), 213–219. Examples
  8. 8. Examples Growth rate Italian firms Bottazzi, Secchi, Physica A 324 (2003), 213–219.
  9. 9. MHRStanley&al,1996Nature379:804–806. Growthrate(someothersetof)USfirms Examples
  10. 10. Universality
  11. 11. Previous models
  12. 12. Previous models Economic models
  13. 13. Previous models Economic models Doesn’t fit e.g. voluntary organizations
  14. 14. Physics models Previous models
  15. 15. Physics models Not without problems either… Previous models
  16. 16. Stochastic models Previous models
  17. 17. Stochastic models Previous models
  18. 18. Stochastic models Original has log- normal growth rate distribution Previous models
  19. 19. The SAF model Assumptions -N individuals connected in a network -G organizations -Each time step an agent changes organization with probability: Schwartzkopf, Axtell, Farmer, arxiv:1004.5397.
  20. 20. The SAF model Assumptions -N individuals connected in a network -G organizations -Each time step an agent changes organization with probability: Claims the network is the key (still trying just one topology)... Schwartzkopf, Axtell, Farmer, arxiv:1004.5397.
  21. 21. The SAF model Assumptions -N individuals connected in a network -G organizations -Each time step an agent changes organization with probability: Claims the network is the key (still trying just one topology)... Non-equilibrium... Schwartzkopf, Axtell, Farmer, arxiv:1004.5397.
  22. 22. The SAF model Assumptions -N individuals connected in a network -G organizations -Each time step an agent changes organization with probability: Claims the network is the key (still trying just one topology)... Non-equilibrium... Hidden parameters... Schwartzkopf, Axtell, Farmer, arxiv:1004.5397.
  23. 23. The SAF model Schwartzkopf, Axtell, Farmer, arxiv:1004.5397. cf. threshold models (Prof. Kertesz’s talk)
  24. 24. The SAF model Schwartzkopf, Axtell, Farmer, arxiv:1004.5397. cf. threshold models (Prof. Kertesz’s talk)
  25. 25. Our extended SAF model Additional assumptions -Trying different networks -Organization cannot die (if the last person leaves a new person joins) -Attachment probability:
  26. 26. Results Tent plot, ER model δ = 1.
  27. 27. Results Tent plot, directed ER model δ = 1.
  28. 28. Results Tent plot, scale-free networks, δ = 1.
  29. 29. Results Tent plot, directed scale-free networks, δ = 1.
  30. 30. Results 2D grid, δ = 0
  31. 31. Results 2D grid, δ = 1
  32. 32. Results 2D grid, δ = 10
  33. 33. Conclusions -The SAF model works and it is independent of the network topology (it just needs a (strongly connected giant component). -The contextual influence parameter makes a difference and can cause the loss of tentity.

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